Overview

Dataset statistics

Number of variables18
Number of observations498
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory70.2 KiB
Average record size in memory144.3 B

Variable types

Numeric13
DateTime1
Categorical4

Alerts

temp is highly overall correlated with maxt and 5 other fieldsHigh correlation
maxt is highly overall correlated with temp and 4 other fieldsHigh correlation
solarenergy is highly overall correlated with temp and 5 other fieldsHigh correlation
mint is highly overall correlated with temp and 5 other fieldsHigh correlation
precip is highly overall correlated with precipcover and 1 other fieldsHigh correlation
solarradiation is highly overall correlated with temp and 5 other fieldsHigh correlation
sealevelpressure is highly overall correlated with mintHigh correlation
dew is highly overall correlated with temp and 1 other fieldsHigh correlation
humidity is highly overall correlated with temp and 5 other fieldsHigh correlation
precipcover is highly overall correlated with precip and 1 other fieldsHigh correlation
cond_Partially-cloudy is highly overall correlated with cond_ClearHigh correlation
cond_Clear is highly overall correlated with cond_Partially-cloudyHigh correlation
cond_Rain is highly overall correlated with precip and 2 other fieldsHigh correlation
cond_Overcast is highly overall correlated with solarenergy and 2 other fieldsHigh correlation
cond_Overcast is highly imbalanced (65.6%)Imbalance
datetimeStr_Date has unique valuesUnique
precip has 418 (83.9%) zerosZeros
precipcover has 418 (83.9%) zerosZeros

Reproduction

Analysis started2023-10-21 13:49:25.306556
Analysis finished2023-10-21 13:49:52.278219
Duration26.97 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

wdir
Real number (ℝ)

Distinct487
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.88847
Minimum31.8
Maximum315.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:52.378374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum31.8
5-th percentile66.242
Q1114.2025
median182.105
Q3243.405
95-th percentile265.7475
Maximum315.33
Range283.53
Interquartile range (IQR)129.2025

Descriptive statistics

Standard deviation68.516834
Coefficient of variation (CV)0.38734482
Kurtosis-1.2795201
Mean176.88847
Median Absolute Deviation (MAD)63.395
Skewness-0.24093393
Sum88090.46
Variance4694.5565
MonotonicityNot monotonic
2023-10-21T15:49:52.516799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255.21 2
 
0.4%
202.88 2
 
0.4%
252.92 2
 
0.4%
262.04 2
 
0.4%
259.13 2
 
0.4%
233.92 2
 
0.4%
256.96 2
 
0.4%
261.5 2
 
0.4%
109.46 2
 
0.4%
180.04 2
 
0.4%
Other values (477) 478
96.0%
ValueCountFrequency (%)
31.8 1
0.2%
42.14 1
0.2%
50.83 1
0.2%
51 1
0.2%
53.46 1
0.2%
53.67 1
0.2%
53.7 1
0.2%
55.63 1
0.2%
55.83 1
0.2%
55.87 1
0.2%
ValueCountFrequency (%)
315.33 1
0.2%
306.88 1
0.2%
277.09 1
0.2%
273.96 1
0.2%
272.43 1
0.2%
272.13 1
0.2%
271.79 1
0.2%
270 1
0.2%
269.79 1
0.2%
268.67 1
0.2%

temp
Real number (ℝ)

Distinct257
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.076104
Minimum1.2
Maximum33.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:52.657392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile5.385
Q19.6
median17
Q324
95-th percentile30.815
Maximum33.9
Range32.7
Interquartile range (IQR)14.4

Descriptive statistics

Standard deviation8.4536308
Coefficient of variation (CV)0.49505617
Kurtosis-1.1610177
Mean17.076104
Median Absolute Deviation (MAD)7.3
Skewness0.14495307
Sum8503.9
Variance71.463875
MonotonicityNot monotonic
2023-10-21T15:49:52.782389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.3 6
 
1.2%
7.1 6
 
1.2%
21.2 5
 
1.0%
10.2 5
 
1.0%
28.3 5
 
1.0%
22.8 5
 
1.0%
9.6 5
 
1.0%
6.6 5
 
1.0%
8.7 5
 
1.0%
11 4
 
0.8%
Other values (247) 447
89.8%
ValueCountFrequency (%)
1.2 1
0.2%
1.6 1
0.2%
1.7 1
0.2%
1.9 1
0.2%
2 1
0.2%
2.1 1
0.2%
2.2 1
0.2%
2.3 1
0.2%
2.5 1
0.2%
3 1
0.2%
ValueCountFrequency (%)
33.9 1
 
0.2%
33.5 1
 
0.2%
33 1
 
0.2%
32.6 1
 
0.2%
32.3 1
 
0.2%
32.2 1
 
0.2%
32.1 3
0.6%
32 1
 
0.2%
31.9 1
 
0.2%
31.8 2
0.4%

maxt
Real number (ℝ)

Distinct215
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.228112
Minimum5.9
Maximum42.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:52.932809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile10.7
Q114.7
median23.25
Q330.275
95-th percentile38.445
Maximum42.8
Range36.9
Interquartile range (IQR)15.575

Descriptive statistics

Standard deviation9.1804344
Coefficient of variation (CV)0.39522946
Kurtosis-1.1888847
Mean23.228112
Median Absolute Deviation (MAD)8.05
Skewness0.18232789
Sum11567.6
Variance84.280375
MonotonicityNot monotonic
2023-10-21T15:49:53.057780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 11
 
2.2%
29 9
 
1.8%
12 9
 
1.8%
13 8
 
1.6%
15 8
 
1.6%
27 8
 
1.6%
26 7
 
1.4%
11.9 6
 
1.2%
33 6
 
1.2%
18 6
 
1.2%
Other values (205) 420
84.3%
ValueCountFrequency (%)
5.9 1
 
0.2%
7.2 1
 
0.2%
8 1
 
0.2%
8.1 2
0.4%
8.2 1
 
0.2%
8.3 1
 
0.2%
8.4 1
 
0.2%
8.9 1
 
0.2%
9 3
0.6%
9.3 1
 
0.2%
ValueCountFrequency (%)
42.8 1
 
0.2%
42.7 1
 
0.2%
41.7 1
 
0.2%
40 1
 
0.2%
39.9 3
0.6%
39.8 1
 
0.2%
39.6 1
 
0.2%
39.2 2
0.4%
39.1 2
0.4%
39 4
0.8%

visibility
Real number (ℝ)

Distinct85
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.517871
Minimum2.5
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:53.198373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile9.185
Q112.5
median14.5
Q314.975
95-th percentile15.9
Maximum16
Range13.5
Interquartile range (IQR)2.475

Descriptive statistics

Standard deviation2.2893907
Coefficient of variation (CV)0.16936029
Kurtosis2.2652998
Mean13.517871
Median Absolute Deviation (MAD)0.8
Skewness-1.5168301
Sum6731.9
Variance5.2413097
MonotonicityNot monotonic
2023-10-21T15:49:53.359216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 36
 
7.2%
14.8 35
 
7.0%
10 33
 
6.6%
14.7 31
 
6.2%
15.9 28
 
5.6%
14.6 27
 
5.4%
14.9 25
 
5.0%
14.2 20
 
4.0%
14.5 17
 
3.4%
15.8 16
 
3.2%
Other values (75) 230
46.2%
ValueCountFrequency (%)
2.5 1
0.2%
3.7 1
0.2%
4.7 1
0.2%
4.8 1
0.2%
5.2 1
0.2%
6.1 1
0.2%
7 1
0.2%
7.1 1
0.2%
7.3 1
0.2%
7.8 1
0.2%
ValueCountFrequency (%)
16 1
 
0.2%
15.9 28
5.6%
15.8 16
3.2%
15.7 8
 
1.6%
15.6 8
 
1.6%
15.5 5
 
1.0%
15.4 6
 
1.2%
15.3 6
 
1.2%
15.2 8
 
1.6%
15.1 3
 
0.6%

wspd
Real number (ℝ)

Distinct162
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.539157
Minimum4.1
Maximum86.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:53.497186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile7.6
Q111.325
median15.7
Q321.1
95-th percentile27.805
Maximum86.1
Range82
Interquartile range (IQR)9.775

Descriptive statistics

Standard deviation7.1492323
Coefficient of variation (CV)0.43226099
Kurtosis17.354188
Mean16.539157
Median Absolute Deviation (MAD)4.75
Skewness2.240164
Sum8236.5
Variance51.111522
MonotonicityNot monotonic
2023-10-21T15:49:53.622127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 23
 
4.6%
9.4 21
 
4.2%
7.6 17
 
3.4%
11.2 12
 
2.4%
13.9 10
 
2.0%
18.4 10
 
2.0%
14.8 10
 
2.0%
8.2 9
 
1.8%
13.7 9
 
1.8%
16.8 9
 
1.8%
Other values (152) 368
73.9%
ValueCountFrequency (%)
4.1 1
 
0.2%
4.9 1
 
0.2%
5.4 7
1.4%
5.8 1
 
0.2%
6.3 1
 
0.2%
6.4 1
 
0.2%
6.5 1
 
0.2%
6.6 1
 
0.2%
6.9 1
 
0.2%
7.1 4
0.8%
ValueCountFrequency (%)
86.1 1
 
0.2%
44.6 1
 
0.2%
37.1 2
0.4%
35.3 1
 
0.2%
32.8 1
 
0.2%
32.2 1
 
0.2%
31.4 1
 
0.2%
30.6 1
 
0.2%
30.4 1
 
0.2%
30 3
0.6%

solarenergy
Real number (ℝ)

Distinct213
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.489357
Minimum2.1
Maximum28.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:53.762745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile4.1
Q19.5
median15.1
Q322.8
95-th percentile26.415
Maximum28.2
Range26.1
Interquartile range (IQR)13.3

Descriptive statistics

Standard deviation7.3991426
Coefficient of variation (CV)0.47769203
Kurtosis-1.2486987
Mean15.489357
Median Absolute Deviation (MAD)6.5
Skewness0.021624544
Sum7713.7
Variance54.747311
MonotonicityNot monotonic
2023-10-21T15:49:53.901159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.7 8
 
1.6%
25.3 7
 
1.4%
13.5 6
 
1.2%
26.3 6
 
1.2%
10.5 6
 
1.2%
23.2 6
 
1.2%
15.6 5
 
1.0%
23.3 5
 
1.0%
11.8 5
 
1.0%
26.4 5
 
1.0%
Other values (203) 439
88.2%
ValueCountFrequency (%)
2.1 1
0.2%
2.2 1
0.2%
2.3 1
0.2%
2.4 2
0.4%
2.5 1
0.2%
2.6 1
0.2%
2.7 2
0.4%
2.8 2
0.4%
2.9 1
0.2%
3.1 1
0.2%
ValueCountFrequency (%)
28.2 2
0.4%
28.1 1
 
0.2%
28 1
 
0.2%
27.7 1
 
0.2%
27.6 1
 
0.2%
27.5 2
0.4%
27.4 2
0.4%
27.3 4
0.8%
27.2 1
 
0.2%
27.1 2
0.4%

mint
Real number (ℝ)

Distinct211
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9246988
Minimum-5.5
Maximum26.3
Zeros3
Zeros (%)0.6%
Negative53
Negative (%)10.6%
Memory size4.0 KiB
2023-10-21T15:49:54.030513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-5.5
5-th percentile-1.645
Q14.325
median9.9
Q315.8
95-th percentile21.2
Maximum26.3
Range31.8
Interquartile range (IQR)11.475

Descriptive statistics

Standard deviation7.2263304
Coefficient of variation (CV)0.72811584
Kurtosis-0.97223112
Mean9.9246988
Median Absolute Deviation (MAD)5.75
Skewness-0.078243193
Sum4942.5
Variance52.219852
MonotonicityNot monotonic
2023-10-21T15:49:54.155484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 11
 
2.2%
3 8
 
1.6%
13.6 7
 
1.4%
6 6
 
1.2%
5.1 6
 
1.2%
16.5 6
 
1.2%
-0.1 5
 
1.0%
7 5
 
1.0%
8.9 5
 
1.0%
11 5
 
1.0%
Other values (201) 434
87.1%
ValueCountFrequency (%)
-5.5 2
0.4%
-4.7 1
 
0.2%
-4.5 1
 
0.2%
-4.4 1
 
0.2%
-4.2 3
0.6%
-4.1 1
 
0.2%
-3.9 1
 
0.2%
-3.2 2
0.4%
-3 2
0.4%
-2.8 1
 
0.2%
ValueCountFrequency (%)
26.3 1
 
0.2%
24 1
 
0.2%
23.2 1
 
0.2%
23 1
 
0.2%
22.9 1
 
0.2%
22.8 2
0.4%
22.5 1
 
0.2%
22.2 4
0.8%
22 2
0.4%
21.9 2
0.4%

precip
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64738956
Minimum0
Maximum37.2
Zeros418
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:54.280455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.4
Maximum37.2
Range37.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1020736
Coefficient of variation (CV)4.7916645
Kurtosis73.861615
Mean0.64738956
Median Absolute Deviation (MAD)0
Skewness7.9054592
Sum322.4
Variance9.6228604
MonotonicityNot monotonic
2023-10-21T15:49:54.389804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 418
83.9%
0.2 16
 
3.2%
0.4 10
 
2.0%
0.8 6
 
1.2%
1 4
 
0.8%
4 3
 
0.6%
0.6 3
 
0.6%
1.4 3
 
0.6%
5.6 2
 
0.4%
3.4 2
 
0.4%
Other values (26) 31
 
6.2%
ValueCountFrequency (%)
0 418
83.9%
0.2 16
 
3.2%
0.4 10
 
2.0%
0.6 3
 
0.6%
0.8 6
 
1.2%
1 4
 
0.8%
1.2 2
 
0.4%
1.4 3
 
0.6%
1.8 1
 
0.2%
2 1
 
0.2%
ValueCountFrequency (%)
37.2 1
0.2%
31.6 1
0.2%
27 1
0.2%
21.8 1
0.2%
14 1
0.2%
12 1
0.2%
11.8 1
0.2%
11.6 1
0.2%
11.2 1
0.2%
8.8 2
0.4%

solarradiation
Real number (ℝ)

Distinct453
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327.56225
Minimum55.1
Maximum557.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:54.530362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum55.1
5-th percentile103.72
Q1243.325
median341.15
Q3436.525
95-th percentile489.13
Maximum557.6
Range502.5
Interquartile range (IQR)193.2

Descriptive statistics

Standard deviation123.29406
Coefficient of variation (CV)0.37639886
Kurtosis-0.88038668
Mean327.56225
Median Absolute Deviation (MAD)97.15
Skewness-0.38168775
Sum163126
Variance15201.425
MonotonicityNot monotonic
2023-10-21T15:49:54.670953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
457.9 3
 
0.6%
307.5 3
 
0.6%
460.3 3
 
0.6%
328.5 2
 
0.4%
520.9 2
 
0.4%
132.3 2
 
0.4%
150.1 2
 
0.4%
352.5 2
 
0.4%
436.3 2
 
0.4%
118.2 2
 
0.4%
Other values (443) 475
95.4%
ValueCountFrequency (%)
55.1 1
0.2%
57.8 1
0.2%
59.4 1
0.2%
63.2 1
0.2%
65.4 1
0.2%
67.3 1
0.2%
67.8 1
0.2%
72.5 1
0.2%
73.2 1
0.2%
75.3 1
0.2%
ValueCountFrequency (%)
557.6 1
0.2%
547.5 1
0.2%
532.4 1
0.2%
528 1
0.2%
525.1 1
0.2%
522.3 1
0.2%
521.6 1
0.2%
520.9 2
0.4%
520.2 1
0.2%
512.3 1
0.2%

sealevelpressure
Real number (ℝ)

Distinct195
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1018.1665
Minimum1001.8
Maximum1033.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:54.795925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1001.8
5-th percentile1010.185
Q11014.3
median1017.7
Q31021.5
95-th percentile1028.43
Maximum1033.1
Range31.3
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation5.4883469
Coefficient of variation (CV)0.005390422
Kurtosis-0.040844747
Mean1018.1665
Median Absolute Deviation (MAD)3.6
Skewness0.30731926
Sum507046.9
Variance30.121952
MonotonicityNot monotonic
2023-10-21T15:49:54.920895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1018.4 8
 
1.6%
1015.5 8
 
1.6%
1021.4 7
 
1.4%
1019 7
 
1.4%
1015.8 7
 
1.4%
1013 7
 
1.4%
1016.4 7
 
1.4%
1014.1 6
 
1.2%
1013.8 6
 
1.2%
1021.9 6
 
1.2%
Other values (185) 429
86.1%
ValueCountFrequency (%)
1001.8 1
0.2%
1003 1
0.2%
1004.5 1
0.2%
1005.1 1
0.2%
1005.2 1
0.2%
1007 2
0.4%
1007.3 1
0.2%
1007.4 2
0.4%
1008.1 1
0.2%
1008.2 2
0.4%
ValueCountFrequency (%)
1033.1 1
0.2%
1032.9 1
0.2%
1032.3 1
0.2%
1032 1
0.2%
1031.8 1
0.2%
1031.7 1
0.2%
1031.3 1
0.2%
1030.7 1
0.2%
1030.5 2
0.4%
1030.1 1
0.2%

dew
Real number (ℝ)

Distinct158
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5893574
Minimum-6.4
Maximum16.7
Zeros1
Zeros (%)0.2%
Negative31
Negative (%)6.2%
Memory size4.0 KiB
2023-10-21T15:49:55.065677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.4
5-th percentile-0.53
Q13.9
median6.9
Q39.275
95-th percentile13.1
Maximum16.7
Range23.1
Interquartile range (IQR)5.375

Descriptive statistics

Standard deviation4.0931738
Coefficient of variation (CV)0.6211795
Kurtosis0.20181887
Mean6.5893574
Median Absolute Deviation (MAD)2.6
Skewness-0.23418693
Sum3281.5
Variance16.754072
MonotonicityNot monotonic
2023-10-21T15:49:55.206268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.1 10
 
2.0%
6.3 10
 
2.0%
7.8 9
 
1.8%
7.6 9
 
1.8%
7 9
 
1.8%
7.3 9
 
1.8%
2.1 8
 
1.6%
3.9 8
 
1.6%
6.4 8
 
1.6%
8.4 7
 
1.4%
Other values (148) 411
82.5%
ValueCountFrequency (%)
-6.4 1
0.2%
-6.3 1
0.2%
-4.7 1
0.2%
-4.4 1
0.2%
-3.9 1
0.2%
-3.6 1
0.2%
-3.4 2
0.4%
-3.2 1
0.2%
-3.1 1
0.2%
-2.9 1
0.2%
ValueCountFrequency (%)
16.7 1
 
0.2%
16.4 3
0.6%
16.2 2
0.4%
15.8 1
 
0.2%
15.2 1
 
0.2%
15.1 2
0.4%
15 1
 
0.2%
14.9 1
 
0.2%
14.2 1
 
0.2%
14 2
0.4%

humidity
Real number (ℝ)

Distinct473
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.95259
Minimum10.49
Maximum97.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:55.346861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10.49
5-th percentile23.1905
Q139.5475
median60.015
Q376.2375
95-th percentile88.2355
Maximum97.78
Range87.29
Interquartile range (IQR)36.69

Descriptive statistics

Standard deviation21.180406
Coefficient of variation (CV)0.36547817
Kurtosis-1.1397081
Mean57.95259
Median Absolute Deviation (MAD)17.935
Skewness-0.16188225
Sum28860.39
Variance448.60962
MonotonicityNot monotonic
2023-10-21T15:49:55.485121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.9 3
 
0.6%
37.6 2
 
0.4%
48.72 2
 
0.4%
75.43 2
 
0.4%
65.1 2
 
0.4%
33.26 2
 
0.4%
42.45 2
 
0.4%
84.17 2
 
0.4%
46.51 2
 
0.4%
60.81 2
 
0.4%
Other values (463) 477
95.8%
ValueCountFrequency (%)
10.49 1
0.2%
13.2 1
0.2%
15.39 1
0.2%
17.4 1
0.2%
17.49 1
0.2%
17.61 1
0.2%
19.05 1
0.2%
19.19 1
0.2%
19.62 1
0.2%
19.72 1
0.2%
ValueCountFrequency (%)
97.78 1
0.2%
96.65 1
0.2%
95.78 1
0.2%
94.91 1
0.2%
94.11 1
0.2%
93.24 1
0.2%
92 1
0.2%
91.92 1
0.2%
91.29 1
0.2%
91.25 1
0.2%

precipcover
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1909839
Minimum0
Maximum79.17
Zeros418
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-10-21T15:49:55.614496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13.5845
Maximum79.17
Range79.17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.3750725
Coefficient of variation (CV)3.3661007
Kurtosis38.61351
Mean2.1909839
Median Absolute Deviation (MAD)0
Skewness5.4602653
Sum1091.11
Variance54.391694
MonotonicityNot monotonic
2023-10-21T15:49:55.723845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 418
83.9%
4.17 26
 
5.2%
8.33 19
 
3.8%
20.83 6
 
1.2%
16.67 5
 
1.0%
12.5 5
 
1.0%
37.5 2
 
0.4%
25 2
 
0.4%
4.35 2
 
0.4%
28 1
 
0.2%
Other values (12) 12
 
2.4%
ValueCountFrequency (%)
0 418
83.9%
4.17 26
 
5.2%
4.35 2
 
0.4%
5.56 1
 
0.2%
8.33 19
 
3.8%
11.76 1
 
0.2%
12.5 5
 
1.0%
13.04 1
 
0.2%
16.67 5
 
1.0%
17.65 1
 
0.2%
ValueCountFrequency (%)
79.17 1
0.2%
58.33 1
0.2%
50 1
0.2%
41.67 1
0.2%
37.5 2
0.4%
35.29 1
0.2%
33.33 1
0.2%
28 1
0.2%
26.09 1
0.2%
25 2
0.4%
Distinct498
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2021-08-02 00:00:00
Maximum2022-12-12 00:00:00
2023-10-21T15:49:55.864438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:55.989408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
309 
0
189 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters498
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 309
62.0%
0 189
38.0%

Length

2023-10-21T15:49:56.130972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T15:49:56.240331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 309
62.0%
0 189
38.0%

Most occurring characters

ValueCountFrequency (%)
1 309
62.0%
0 189
38.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 498
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 309
62.0%
0 189
38.0%

Most occurring scripts

ValueCountFrequency (%)
Common 498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 309
62.0%
0 189
38.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 309
62.0%
0 189
38.0%

cond_Clear
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
342 
1
156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters498
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 342
68.7%
1 156
31.3%

Length

2023-10-21T15:49:56.334052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T15:49:56.443375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 342
68.7%
1 156
31.3%

Most occurring characters

ValueCountFrequency (%)
0 342
68.7%
1 156
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 498
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 342
68.7%
1 156
31.3%

Most occurring scripts

ValueCountFrequency (%)
Common 498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 342
68.7%
1 156
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 342
68.7%
1 156
31.3%

cond_Rain
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
418 
1
80 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters498
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 418
83.9%
1 80
 
16.1%

Length

2023-10-21T15:49:56.543726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T15:49:56.648626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 418
83.9%
1 80
 
16.1%

Most occurring characters

ValueCountFrequency (%)
0 418
83.9%
1 80
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 498
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 418
83.9%
1 80
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common 498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 418
83.9%
1 80
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 418
83.9%
1 80
 
16.1%

cond_Overcast
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
466 
1
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters498
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 466
93.6%
1 32
 
6.4%

Length

2023-10-21T15:49:56.742354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T15:49:56.851704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 466
93.6%
1 32
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 466
93.6%
1 32
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 498
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 466
93.6%
1 32
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 466
93.6%
1 32
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 466
93.6%
1 32
 
6.4%

Interactions

2023-10-21T15:49:49.548814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:26.062356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:28.150125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:30.290288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:32.401606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:34.696419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:36.602921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:38.385808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:40.249443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:42.199061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:44.101094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:46.019648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:47.794578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:49.703884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:26.210487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:28.307385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:30.460948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:32.578862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:34.835963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:36.749530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:38.526477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:40.402446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:42.334599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:44.256412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:46.150269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:47.916996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:49.865450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:26.366759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:28.462888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:30.628426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:32.773230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:34.969073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:36.887227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:38.683784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:40.550992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:42.468334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:44.404885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:46.282910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:48.065607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:50.014162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:26.548651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:28.604918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:30.769944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:32.977126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:35.117110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:37.035613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:38.832157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:40.730438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:42.618063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:44.561899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:46.429081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:48.216666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:50.190487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:26.734869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:28.753910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:30.932805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:33.179762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:35.254823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:37.186252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:38.969151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:40.882899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:42.898777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:44.711047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:46.576431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:48.358680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:50.334669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:26.892256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:28.901302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:31.069373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:33.349958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:35.410649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:37.302909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:39.106290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:41.019602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:43.019228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:44.835221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:46.702830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:48.483008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:50.478791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:27.044394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:29.034032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:31.224283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:33.514156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:35.550674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:37.438367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:39.251379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:41.151837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:43.153469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:44.968099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:46.831900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:48.613341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:50.633694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:27.193433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:29.294299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:31.374434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:33.697402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:35.792619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:37.569915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:39.397648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:41.311354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:43.292562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:45.117610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:46.969536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:48.751788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:50.785378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:27.325286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:29.492488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:31.519807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:33.924193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:35.922339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:37.720595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:39.544609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:41.464947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:43.433007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:45.268537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:47.112425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:48.894977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:50.931746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:27.475805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:29.639783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:31.674777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:34.105119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:36.064134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:37.858132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:39.686416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:41.608050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:43.568227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:45.416363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:47.240252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:49.016579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:51.082741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:27.666250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:29.801471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:31.848552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:34.252208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:36.186356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:37.992432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:39.817906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:41.754147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:43.708563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:45.549232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:47.368006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:49.158546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:51.239675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:27.832873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:29.946690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:32.014040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:34.387244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:36.306322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:38.118202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:39.965028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:41.897088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:43.835411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:45.702008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:47.502311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:49.283067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:51.546423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:27.989020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:30.123557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:32.223365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:34.534029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:36.453095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:38.235874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:40.098820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:42.039330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:43.950571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:45.859272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:47.642464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:49:49.408320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-10-21T15:49:57.179758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
wdirtempmaxtvisibilitywspdsolarenergymintprecipsolarradiationsealevelpressuredewhumidityprecipcovercond_Partially-cloudycond_Clearcond_Raincond_Overcast
wdir1.0000.2300.1460.2420.3090.1390.2910.0790.122-0.3770.114-0.1800.0750.1140.1670.1370.136
temp0.2301.0000.9650.2870.1400.7090.939-0.1960.662-0.4790.521-0.834-0.1990.0820.2010.3050.213
maxt0.1460.9651.0000.3130.0270.7640.848-0.3080.729-0.3580.402-0.887-0.3130.1020.2270.2960.265
visibility0.2420.2870.3131.0000.0910.4230.259-0.1840.419-0.230-0.019-0.436-0.1890.0830.1230.2070.345
wspd0.3090.1400.0270.0911.000-0.0250.2640.242-0.086-0.3830.188-0.0130.2390.1340.2190.2210.227
solarenergy0.1390.7090.7640.423-0.0251.0000.538-0.4170.971-0.2690.077-0.816-0.4260.1940.2880.4910.593
mint0.2910.9390.8480.2590.2640.5381.000-0.0290.478-0.5800.643-0.678-0.0320.2020.2750.2210.125
precip0.079-0.196-0.308-0.1840.242-0.417-0.0291.000-0.439-0.2850.2210.4330.9980.0490.0950.5010.332
solarradiation0.1220.6620.7290.419-0.0860.9710.478-0.4391.000-0.2100.046-0.787-0.4470.2630.3510.5070.644
sealevelpressure-0.377-0.479-0.358-0.230-0.383-0.269-0.580-0.285-0.2101.000-0.3960.272-0.2840.1660.2020.3400.130
dew0.1140.5210.402-0.0190.1880.0770.6430.2210.046-0.3961.000-0.0430.2190.2340.3000.2090.000
humidity-0.180-0.834-0.887-0.436-0.013-0.816-0.6780.433-0.7870.272-0.0431.0000.4370.1580.3210.4400.503
precipcover0.075-0.199-0.313-0.1890.239-0.426-0.0320.998-0.447-0.2840.2190.4371.0000.1570.1900.7620.428
cond_Partially-cloudy0.1140.0820.1020.0830.1340.1940.2020.0490.2630.1660.2340.1580.1571.0000.8590.1380.324
cond_Clear0.1670.2010.2270.1230.2190.2880.2750.0950.3510.2020.3000.3210.1900.8591.0000.2860.162
cond_Rain0.1370.3050.2960.2070.2210.4910.2210.5010.5070.3400.2090.4400.7620.1380.2861.0000.227
cond_Overcast0.1360.2130.2650.3450.2270.5930.1250.3320.6440.1300.0000.5030.4280.3240.1620.2271.000

Missing values

2023-10-21T15:49:51.809828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-21T15:49:52.118717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

wdirtempmaxtvisibilitywspdsolarenergymintprecipsolarradiationsealevelpressuredewhumidityprecipcoverdatetimeStr_Datecond_Partially-cloudycond_Clearcond_Raincond_Overcast
0247.1324.0030.3014.8010.6024.4014.200.00452.701,014.103.8027.870.002021-08-021000
1205.2625.8033.0014.6012.4024.7015.300.00456.501,015.107.7035.150.002021-08-031000
2264.8327.3033.0014.8016.2025.3021.200.00468.301,014.608.0030.150.002021-08-040100
3261.2528.4035.1014.8020.5025.3019.600.00468.401,014.805.5025.730.002021-08-050100
4253.9628.6034.9014.8029.7025.5022.200.00471.701,010.705.8025.600.002021-08-060100
5261.2924.3030.0015.9020.3025.2017.200.00499.401,010.107.6037.090.002021-08-070100
6233.9225.0033.0015.9013.0025.2014.700.00499.101,012.705.5031.240.002021-08-080100
7154.7126.1033.3014.4017.6025.0015.200.00463.101,014.5011.8046.640.002021-08-091000
8124.1728.3035.2014.5011.2020.8021.200.00412.401,014.1015.0047.260.002021-08-101000
9110.2130.0038.8012.6022.6016.3020.800.00348.301,014.6013.0037.680.002021-08-111000
wdirtempmaxtvisibilitywspdsolarenergymintprecipsolarradiationsealevelpressuredewhumidityprecipcoverdatetimeStr_Datecond_Partially-cloudycond_Clearcond_Raincond_Overcast
488213.446.3013.006.1012.109.00-2.000.00250.801,007.402.1077.430.002022-12-031000
489244.2710.8014.0010.0019.308.005.000.00222.201,009.706.4075.800.002022-12-041000
49089.888.9011.609.2011.102.304.002.6063.201,012.008.1094.9135.292022-12-051010
491218.0410.8014.008.209.405.609.000.60155.701,015.209.3090.658.332022-12-061010
492126.1911.0013.9013.307.605.708.000.00157.101,013.808.7086.540.002022-12-071000
493144.0010.8013.209.5011.402.808.9011.8077.401,005.209.9094.1141.672022-12-081010
494214.1311.0014.0014.3019.606.209.907.00172.401,001.809.5090.6520.832022-12-091010
495255.2210.5013.0014.3021.504.308.900.60119.001,009.808.4087.2613.042022-12-101010
496220.5812.0015.7011.4028.604.108.4031.60114.901,007.3010.6091.0337.502022-12-110011
497240.6314.5016.9012.7028.403.1012.603.4087.401,009.8012.1086.1212.502022-12-120011